Online estimation of the state of charge of a lithium-ion battery based on the fusion model

WANG Xiao-lan; JIN Hao-qing; LIU Xiang-yuan

doi:10.13374/j.issn2095-9389.2019.09.20.001

Volume 42 Issue 9

Sep. 2020

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Engineering > 2020 > 42(9): 1200-1208

WANG Xiao-lan, JIN Hao-qing, LIU Xiang-yuan. Online estimation of the state of charge of a lithium-ion battery based on the fusion model[J]. Chinese Journal of Engineering, 2020, 42(9): 1200-1208. doi: 10.13374/j.issn2095-9389.2019.09.20.001

Citation:

WANG Xiao-lan, JIN Hao-qing, LIU Xiang-yuan. Online estimation of the state of charge of a lithium-ion battery based on the fusion model[J]. Chinese Journal of Engineering, 2020, 42(9): 1200-1208. doi: 10.13374/j.issn2095-9389.2019.09.20.001

Citation:

PDF( 1378 KB)

Online estimation of the state of charge of a lithium-ion battery based on the fusion model

doi: 10.13374/j.issn2095-9389.2019.09.20.001

WANG Xiao-lan^{1, 2, 3
,
,},
JIN Hao-qing^{1, 2, 3},
LIU Xiang-yuan^{1, 2, 3}

1.
College of Electrical and Information Engineering Lanzhou University of Technology, Lanzhou 730050, China
2.
Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou 730050, China
3.
National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou 730050, China

More Information

Corresponding author: E-mail: wangzt@lut.cn
Received Date: 2019-09-20
Publish Date: 2020-09-20

Abstract

Abstract

In the context of the global response to environmental pollution and climate change, countries have begun to pay attention to energy system reform and economic development to ensure low carbon transition. Among them, the development of low carbon transportation has become an important aspect of green transportation system construction. The development of electric vehicle technology can effectively reduce energy consumption and environmental pollution. However, with the recent reports of new energy vehicle safety accidents at home and abroad, the safety of lithium-ion batteries has attracted increasing attention from the industry. To prevent overcharging and overdischarging from affecting battery life and safety during use, a complete battery management system is required to control and manage a lithium-ion battery. The state of charge (SOC) used to reflect the remaining capacity of a battery is one of the key parameters. Therefore, an accurate SOC value is of significance to the safety of lithium-ion battery use and the safety performance of new energy vehicles. The low online estimation accuracy of the SOC of lithium-ion batteries and the estimation accuracy of the equivalent circuit model method are inconsistent with the model complexity. This study improved the extended Kalman filtering (EKF) algorithm and established a SOC estimation error prediction model based on the extreme learning machine (ELM) algorithm, which used the operating voltage and current of the battery as input and the SOC estimation error of the equivalent circuit model method as the output. On the basis of the physical data fusion method and the error prediction model, the online estimation model of the lithium-ion battery SOC based on the equivalent circuit model method combined with the ELM was established. The simulation results showed that the improved EKF algorithm enhances the estimation precision of the algorithm. Moreover, the physical data fusion model reduces the estimation error introduced by voltage and current measurements, overcomes the contradiction between the estimation accuracy and complexity of the equivalent circuit model method, improves the estimation accuracy of the SOC, and meets the application requirement that the estimation error must be less than 5%.
- lithium-ion battery,
- stage of charge estimation,
- extended Kalman filtering,
- extreme learning machine,
- fusion model

FullText(HTML)

References(28)

References

[1]	鳳振華, 王雪成, 張海穎, 等. 低碳視角下綠色交通發展路徑與政策研究. 交通運輸研究, 2019, 5(4):37 Feng Z H, Wang X C, Zhang H Y, et al. Path and policy of green transportation development from low carbon perspective. <italic>Transp Res</italic>, 2019, 5(4): 37
[2]	張美迪. 電動汽車電池的現狀及發展趨勢. 內燃機與配件, 2019(15):230 Zhang M D. Current status and development trend of electric vehicle batteries. <italic>Internal Combust Engine Parts</italic>, 2019(15): 230
[3]	姜久春, 高洋, 張彩萍, 等. 電動汽車鋰離子動力電池健康狀態在線診斷方法. 機械工程學報, 2019, 55(20):60 Jiang J C, Gao Y, Zhang C P, et al. Online diagnostic method for health status of lithium-ion battery in electric vehicle. <italic>J Mech Eng</italic>, 2019, 55(20): 60
[4]	符曉玲, 商云龍, 崔納新. 電動汽車電池管理系統研究現狀及發展趨勢. 電力電子技術, 2011, 45(12):27 Fu X L, Shang Y L, Cui N X. Research and development trend on battery management system for EV. <italic>Power Electron</italic>, 2011, 45(12): 27
[5]	譚澤富, 孫榮利, 楊芮, 等. 電池管理系統發展綜述. 重慶理工大學學報: 自然科學, 2019, 33(9):40 Tan Z F, Sun R L, Yang R, et al. Overview of battery management system. <italic>J Chongqing Univ Technol Nat Sci</italic>, 2019, 33(9): 40
[6]	張持健, 陳航. 鋰電池SOC預測方法綜述. 電源技術, 2016, 40(6):1318 Zhang C J, Chen H. Review of state of charge estimation methods for lithium battery. <italic>Chin J Power Sources</italic>, 2016, 40(6): 1318
[7]	顏湘武, 鄧浩然, 郭琪, 等. 基于自適應無跡卡爾曼濾波的動力電池健康狀態檢測及梯次利用研究. 電工技術學報, 2019, 34(18):3937 Yan X W, Deng H R, Guo Q, et al. Study on the state of health detection of power batteries based on adaptive unscented Kalman filters and the battery echelon utilization. <italic>Trans China Electrotech Soc</italic>, 2019, 34(18): 3937
[8]	盧蘭光, 李建秋, 華劍鋒, 等. 電動汽車鋰離子電池管理系統的關鍵技術. 科技導報, 2016, 34(6):39 Lu L G, Li J Q, Hua J F, et al. A review on the key issues of the lithium-ion battery management. <italic>Sci Technol Rev</italic>, 2016, 34(6): 39
[9]	夏超英, 張術, 孫宏濤. 基于推廣卡爾曼濾波算法的SOC估算策略. 電源技術, 2007, 31(5):414 Xia C Y, Zhang S, Sun H T. A strategy of estimating state of charge based on extended Kalman filter. <italic>Chin J Power Sources</italic>, 2007, 31(5): 414
[10]	李澤洋, 李振強, 呂豐. 基于UKF方法的鋰離子電池荷電狀態估計研究. 廣西科技大學學報, 2019, 30(3):41 Li Z Y, Li Z Q, Lv F. State of charge estimation of lithium-ion battery based on UKF method. <italic>J J Guangxi Univ Sci Technol</italic>, 2019, 30(3): 41
[11]	Johnson V H. Battery performance models in ADVISOR. <italic>J Power Sources</italic>, 2002, 110(2): 321 doi: 10.1016/S0378-7753(02)00194-5
[12]	Salameh Z M, Casacca M A, Lynch W A. A mathematical model for lead-acid batteries. <italic>IEEE Trans Energy Convers</italic>, 1992, 7(1): 93 doi: 10.1109/60.124547
[13]	張利, 張慶, 常成, 等. 用于電動汽車SOC估計的等效電路模型研究. 電子測量與儀器學報, 2014, 28(10):1161 Zhang L, Zhang Q, Chang C, et al. Research on equivalent circuit model for state of charge estimation of electric vehicle. <italic>J Electron Meas Instrum</italic>, 2014, 28(10): 1161
[14]	任育涵. 動力電池SoC在線估計方法研究[學位論文]. 天津: 河北工業大學, 2017 Ren Y H. The Research on the On-Line SOC Estimation Method for Power Battery[Dissertation]. Tianjin: Hebei University of Technology, 2017
[15]	張衛平, 雷歌陽, 張曉強. 一種簡化的鋰離子電池SOC估計方法. 電源技術, 2016, 40(7):1359 Zhang W P, Lei G Y, Zhang X Q. A simplified Li-ion battery SOC estimation method. <italic>Chin J Power Sources</italic>, 2016, 40(7): 1359
[16]	谷苗, 夏超英, 田聰穎. 基于綜合型卡爾曼濾波的鋰離子電池荷電狀態估算. 電工技術學報, 2019, 34(2):419 Gu M, Xia C Y, Tian C Y. Li-ion battery state of charge estimation based on comprehensive Kalman filter. <italic>Trans China Electrotech Soc</italic>, 2019, 34(2): 419
[17]	任軍, 王凱, 任寶森. 基于改進模型和無跡卡爾曼濾波的鋰離子電池荷電狀態估計. 電器與能效管理技術, 2019(4):64 Ren J, Wang K, Ren B S. State of charge estimation of lithium-ion battery based on improved model and unscented Kalman filter. <italic>Electr Energy Manage Technol</italic>, 2019(4): 64
[18]	錢能, 嚴運兵, 李文杰, 等. 磷酸鐵鋰鋰離子電池Thevenin等效模型的改進. 電池, 2018, 48(4):257 Qian N, Yan Y B, Li W J, et al. Improving of Thevenin equivalent model for lithium iron phosphate Li-ion battery. <italic>Battery Bimonthly</italic>, 2018, 48(4): 257
[19]	蔡信, 李波, 汪宏華, 等. 基于神經網絡模型的動力電池SOC估計研究. 機電工程, 2015, 32(1):128 Cai X, Li B, Wang H H, et al. Estimation of state-of-charge for electric vehicle power battery with neural network method. <italic>Mech Electr Eng Mag</italic>, 2015, 32(1): 128
[20]	雷肖, 陳清泉, 劉開培, 等. 電動車蓄電池荷電狀態估計的神經網絡方法. 電工技術學報, 2007, 22(8):155 Lei X, Chen Q Q, Liu K P, et al. Battery state of charge estimation based on neural-network for electric vehicles. <italic>Trans China Electrotech Soc</italic>, 2007, 22(8): 155
[21]	范興明, 王超, 張鑫, 等. 基于增量學習相關向量機的鋰離子電池SOC預測方法. 電工技術學報, 2019, 34(13):2700 Fan X M, Wang C, Zhang X, et al. A prediction method of Li-ion batteries SOC based on incremental learning relevance vector machine. <italic>Trans China Electrotech Soc</italic>, 2019, 34(13): 2700
[22]	宋紹劍, 王志浩, 林小峰. 基于極限學習機的磷酸鐵鋰電池SOC估算研究. 電源技術, 2018, 42(6):806 Song S J, Wang Z H, Lin X F. Research on SOC estimation of LiFePO4 batteries based on ELM. <italic>Chin J Power Sources</italic>, 2018, 42(6): 806
[23]	張易航, 王鼎, 肖圍, 等. 鋰離子電池SOC估算方法概況及難點分析. 電源技術, 2019, 43(11):1894 Zhang Y H, Wang D, Xiao W, et al. Review of SOC estimation and difficulties in Li-ion battery. <italic>Chin J Power Sources</italic>, 2019, 43(11): 1894
[24]	朱政. 磷酸鐵鋰電池荷電狀態估計方法的研究[學位論文]. 哈爾濱: 哈爾濱工業大學, 2013 Zhu Z. Research on SOC Estimation of LifeP04 Battery[Dissertation]. Harbin: Harbin Institute of Technology, 2013
[25]	張禹軒. 電動汽車動力電池模型參數在線辨識及SOC估計[學位論文]. 長春: 吉林大學, 2014 Zhang Y X. Parameter Identification and SOC Estimation of Power Battery for Electric Vehicle[Dissertation]. Changchun: Jilin University, 2014
[26]	王學斌, 徐建宏, 張章. 卡爾曼濾波器參數分析與應用方法研究. 計算機應用與軟件, 2012, 29(6):212 Wang X B, Xu J H, Zhang Z. On analysis and application approach for Kalman filter parameters. <italic>Comput Appl Software</italic>, 2012, 29(6): 212
[27]	Feng G R, Huang G B, Lin Q P, et al. Error minimized extreme learning machine with growth of hidden nodes and incremental learning. <italic>IEEE Trans Neural Networks</italic>, 2009, 20(8): 1352 doi: 10.1109/TNN.2009.2024147
[28]	Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: a new learning scheme of feedforward neural networks//Proceedings of 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541). Budapest, Hungary, 2004: 985